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Table 1 Construction process of the uncertain C4.5 decision tree

From: P2P net loan default risk based on Spark and complex network analysis based on wireless network element data environment

Input: the indefinite set of data D, all the attributes list attribute_list contained in D
Output: uncertain decision tree
Start:
1) create a node N;
2) If indeterminate dataset D all the tuple class labels are C;
3) return to N as a leaf node and mark as a class C;
4) Else if (attribute_list empty) then
5) return to the N node and mark with the majority of the class marks in the remaining tuples;
6) End if;
7) the information gain rate of each attribute is calculated, and the highest information gain rate is selected as the N point.
8) If (attribute is continuous or uncertain) then
9) select a split position Y;
10) For (R per unit of tuple) do
11) If (attribute = y) then
12) the weight of lD is w Rj.
13) Else if (attribute>y) then
14) the weight of rD is w Rj.
15) Else
16) to take the weight of lD from yxjdxxfw R
17) to take the weight of rD from (.Xyjdxxfw R 2)
18) End if;
19) End for;
20) Else For
21) each discrete attribute value NIA),..., 3,2,1 (I from do)
22) a direct downward division of iD branches;
23) End for;
24) End if;
25) For (each iD) do
26) according to the division rules of the decision tree, the nodes continue to be divided.
27) delete the attributes that have been partitioned from attribute_list after each partition.
28) End for;
29) End